mlsanityData to 14 Oct 2026

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

arXiv 2209.13777 ↗First posted 28 Sep 2022cs.CV cs.LGcited by 10

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Abstract (arXiv)

Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated methods have been developed to address the challenges this problem comprises. In this paper, we propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective, and then augment the extremely label-constrained support set in few-shot classification tasks. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it is able to outperform state-of-the-art methods on four benchmark datasets.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 32 of its 59 references that are in the map. First posted on arXiv 28 Sep 2022.

Builds on

32 of 59 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  5. 2016Matching Networks for One Shot Learningcited by 2,255
  6. 2019A Closer Look at Few-shot Classificationcited by 684
  7. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  8. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  9. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 437
  10. 2018Meta-learning with differentiable closed-form solverscited by 397
  11. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  12. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 294
20 more
  1. 2016Adversarial Training Methods for Semi-Supervised Text Classificationcited by 280
  2. 2019Cross Attention Network for Few-shot Classificationcited by 219
  3. 2022Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labelscited by 218
  4. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 211
  5. 2019NLNL: Negative Learning for Noisy Labelscited by 151
  6. 2021Fine-Grained Image Analysis with Deep Learning: A Surveycited by 135
  7. 2020Few-Shot Classification with Feature Map Reconstruction Networkscited by 116
  8. 2017Learning from Complementary Labelscited by 88
  9. 2019Learning to Self-Train for Semi-Supervised Few-Shot Classificationcited by 83
  10. 2020Embedding Propagation: Smoother Manifold for Few-Shot Classificationcited by 80
  11. 2021Relational Embedding for Few-Shot Classificationcited by 74
  12. 2020Instance Credibility Inference for Few-Shot Learningcited by 57
  13. 2021Rectifying the Shortcut Learning of Background for Few-Shot Learningcited by 51
  14. 2022Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classificationcited by 50
  15. 2020Iterative label cleaning for transductive and semi-supervised few-shot learningcited by 40
  16. 2019TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learningcited by 39
  17. 2019Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learningcited by 29
  18. 2022Matching Feature Sets for Few-Shot Image Classificationcited by 25
  19. 2021Learning to Affiliate: Mutual Centralized Learning for Few-shot Classificationcited by 18
  20. 2020How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learningcited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 1; 2026: 2.

10 papers.

  1. 2023Revisiting Few-Shot Object Detection with Vision-Language Modelscited by 32
  2. 2023Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practicalcited by 10
  3. 2023Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labelscited by 8
  4. 2024Enhancing Vision-Language Few-Shot Adaptation with Negative Learningcited by 8
  5. 2023Exploring Active Learning in Meta-Learning: Enhancing Context Set Labelingcited by 1
  6. 2024Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregationcited by 0
  7. 2024Improving 3D Few-Shot Segmentation with Inference-Time Pseudo-Labelingcited by 0
  8. 2025Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspectivecited by 0
  9. 2026Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutionscited by 0
  10. 2026Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Groundingcited by 0